Purpose
The RMG Insights & Analytics Leader turns complex commercial, distributor, pricing, revenue, consumer and market data into scalable Power BI solutions and actionable business insights. The role optimizes existing reporting, builds new analytical products and semantic models, and enables RMG and Sales teams to make faster, fact-based decisions across a diverse distributor network.
The position combines strong Power BI capability with data engineering awareness, commercial curiosity and the flexibility to respond rapidly as business questions, datasets and priorities evolve.
Required
- Bachelor's degree in Data Analytics, Computer Science, Information Systems, Engineering, Economics, Finance, Business Analytics or a comparable quantitative discipline.
- Hands-on experience developing Power BI solutions, including data modelling, DAX, Power Query, visualization design, publishing and performance optimization.
- Experience working with large and complex datasets and translating ambiguous business questions into structured analytical solutions.
- Understanding of dimensional modelling, semantic layers, data quality principles and cloud-based data environments.
- Ability to communicate analytical findings clearly to technical and non-technical stakeholders in English.
- Demonstrated success working in a collaborative, cross-functional and rapidly changing environment.
Skills and Knowledge
- Power BI is the primary skill. Strong data modelling, analytical problem-solving and report performance optimization are essential.
- Connects data to commercial decisions and explains complex analysis in practical, actionable language.
- Asks clarifying questions, confirms understanding and does not proceed on assumptions when requirements are unclear.
- Rapidly learns new tools, datasets and business contexts; stays current with evolving Power BI, cloud and AI capabilities.
- Comfortable re-prioritizing or redesigning solutions when sales plans and stakeholder needs change quickly.
- Works effectively across cultures, time zones and technical/non-technical teams; contributes to shared models and standards.
- Balances speed with data quality, documents decisions and delivers sustainable solutions rather than one-off reports.